Early, unplanned readmission in diabetes inpatients: Comparing the utility and performance of machine learning and traditional prediction models to guide timely diabetes management team review.

ABSTRACT:

Background

Inpatients with diabetes have higher early unplanned readmission (EUR) rates. Diabetes management team (DMT) review reduces EUR. While glucose-based patient selection for DMT reduces in-hospital adverse outcomes, this single criterion is a suboptimal predictor of EUR.

Aim

We developed, compared and externally validated four EUR prediction models in diabetes inpatients using primarily early admission data to facilitate timely review. Secondarily, we investigated how combining predictive and glucose data can refine patient selection for DMT review.

Methods

We constructed three traditional models (classification tree, logistic group lasso, elastic net) and a neural network model using 14 routinely available variables. Models were externally validated and perfo

SUBMITTER: Lau SM 

PROVIDER: S-EPMC12857872 | biostudies-literature | 2026 Feb

REPOSITORIES: biostudies-literature